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@@ -1,170 +0,0 @@
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-# main imports
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-import logging
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-
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-# Generic algorithm class
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-class Algorithm():
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-
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- def __init__(self, _initalizer, _evaluator, _operators, _policy, _validator, _maximise=True, _parent=None):
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- """
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- Initialize all usefull parameters for problem to solve
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- """
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-
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- self.initializer = _initalizer
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- self.evaluator = _evaluator
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- self.operators = _operators
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- self.validator = _validator
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- self.policy = _policy
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- self.checkpoint = None
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-
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- # other parameters
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- self.parent = _parent # parent algorithm if it's sub algorithm
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- #self.maxEvaluations = 0 # by default
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- self.maximise = _maximise
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-
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- self.initRun()
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-
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-
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- def addCheckpoint(self, _class, _every, _filepath):
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- self.checkpoint = _class(self, _every, _filepath)
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-
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-
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- def setCheckpoint(self, _checkpoint):
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- self.checkpoint = _checkpoint
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-
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-
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- def resume(self):
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- if self.checkpoint is None:
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- raise ValueError("Need to `addCheckpoint` or `setCheckpoint` is you want to use this process")
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- else:
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- print('Checkpoint loading is called')
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- self.checkpoint.load()
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-
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-
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- def initRun(self):
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- """
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- Reinit the whole variables
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- """
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-
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- self.currentSolution = self.initializer()
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-
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- # evaluate current solution
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- self.currentSolution.evaluate(self.evaluator)
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-
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- # keep in memory best known solution (current solution)
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- self.bestSolution = self.currentSolution
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-
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-
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- def increaseEvaluation(self):
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- self.numberOfEvaluations += 1
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-
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- if self.parent is not None:
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- self.parent.numberOfEvaluations += 1
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-
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-
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- def getGlobalEvaluation(self):
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-
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- if self.parent is not None:
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- return self.parent.numberOfEvaluations
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-
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- return self.numberOfEvaluations
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-
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-
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- def stop(self):
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- """
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- Global stopping criteria (check for inner algorithm too)
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- """
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- if self.parent is not None:
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- return self.parent.numberOfEvaluations >= self.parent.maxEvaluations or self.numberOfEvaluations >= self.maxEvaluations
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-
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- return self.numberOfEvaluations >= self.maxEvaluations
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-
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-
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- def evaluate(self, solution):
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- """
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- Returns:
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- fitness score of solution which is not already evaluated or changed
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-
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- Note:
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- if multi-objective problem this method can be updated using array of `evaluator`
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- """
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- return solution.evaluate(self.evaluator)
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-
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-
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- def update(self, solution, secondSolution=None):
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- """
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- Apply update function to solution using specific `policy`
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-
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- Check if solution is valid after modification and returns it
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-
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- Returns:
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- updated solution
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- """
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-
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- # two parameters are sent if specific crossover solution are wished
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- sol = self.policy.apply(solution, secondSolution)
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-
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- if(sol.isValid(self.validator)):
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- return sol
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- else:
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- logging.info("-- New solution is not valid %s" % sol)
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- return solution
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-
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-
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- def isBetter(self, solution):
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- """
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- Check if solution is better than best found
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-
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- Returns:
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- `True` if better
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- """
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- # depending of problem to solve (maximizing or minimizing)
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- if self.maximise:
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- if self.evaluate(solution) > self.bestSolution.fitness():
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- return True
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- else:
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- if self.evaluate(solution) < self.bestSolution.fitness():
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- return True
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-
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- # by default
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- return False
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-
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-
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- def run(self, _evaluations):
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- """
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- Run the specific algorithm following number of evaluations to find optima
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- """
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-
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- self.maxEvaluations = _evaluations
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-
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- self.initRun()
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-
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- # check if global evaluation is used or not
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- if self.parent is not None and self.getGlobalEvaluation() != 0:
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-
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- # init number evaluations of inner algorithm depending of globalEvaluation
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- # allows to restart from `checkpoint` last evaluation into inner algorithm
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- rest = self.getGlobalEvaluation() % self.maxEvaluations
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- self.numberOfEvaluations = rest
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-
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- else:
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- self.numberOfEvaluations = 0
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-
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- logging.info("Run %s with %s evaluations" % (self.__str__(), _evaluations))
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-
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-
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- def progress(self):
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-
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- if self.checkpoint is not None:
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- self.checkpoint.run()
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-
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- logging.info("-- %s evaluation %s of %s (%s%%) - BEST SCORE %s" % (type(self).__name__, self.numberOfEvaluations, self.maxEvaluations, "{0:.2f}".format((self.numberOfEvaluations) / self.maxEvaluations * 100.), self.bestSolution.fitness()))
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-
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-
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- def information(self):
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- logging.info("-- Best %s - SCORE %s" % (self.bestSolution, self.bestSolution.fitness()))
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-
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-
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- def __str__(self):
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- return "%s using %s" % (type(self).__name__, type(self.bestSolution).__name__)
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-
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-
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